{
  "id": "2007.05074",
  "version": "v1",
  "published": "2020-07-09T21:26:09.000Z",
  "updated": "2020-07-09T21:26:09.000Z",
  "title": "Learning dynamical systems from data: a simple cross-validation perspective",
  "authors": [
    "Boumediene Hamzi",
    "Houman Owhadi"
  ],
  "comment": "File uploaded on arxiv on Sunday, July 5th, 2020. Got delayed due to tex problems on ArXiv. Original version at https://www.researchgate.net/publication/342693818_Learning_dynamical_systems_from_data_a_simple_cross-validation_perspective",
  "doi": "10.13140/RG.2.2.24823.44964",
  "categories": [
    "cs.LG",
    "math.DS",
    "nlin.CD",
    "stat.CO",
    "stat.ML"
  ],
  "abstract": "Regressing the vector field of a dynamical system from a finite number of observed states is a natural way to learn surrogate models for such systems. We present variants of cross-validation (Kernel Flows \\cite{Owhadi19} and its variants based on Maximum Mean Discrepancy and Lyapunov exponents) as simple approaches for learning the kernel used in these emulators.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-07-09T21:26:09.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "learning dynamical systems",
      "simple cross-validation perspective",
      "learn surrogate models",
      "maximum mean discrepancy",
      "finite number"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}